Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

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Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica

10.5194/egusphere-egu2020-1715 ◽

2020 ◽

Author(s):

Jiechen Zhao ◽

Bin Cheng ◽

Timo Vihma ◽

Qinghua Yang ◽

Fengming Hui ◽

...

Keyword(s):

Sea Ice ◽

Snow Cover ◽

Annual Cycle ◽

Snow Depth ◽

Snow Accumulation ◽

East Antarctica ◽

Prydz Bay ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Landfast Sea Ice

<p>The observed snow depth and ice thickness on landfast sea ice in Prydz Bay, East Antarctica, were used to determine the role of snow in (a) the annual cycle of sea ice thickness at a fixed location (SIP) where snow usually blows away after snowfall and (b) early summer sea ice thickness within the transportation route surveys (TRS) domain farther from coast, where annual snow accumulation is substantial. The annual mean snow depth and maximum ice thickness had a negative relationship (r = &#8722;0.58, p < 0.05) at SIP, indicating a primary insulation effect of snow on ice thickness. However, in the TRS domain, this effect was negligible because snow contributes to ice thickness. A one-dimensional thermodynamic sea ice model, forced by local weather observations, reproduced the annual cycle of ice thickness at SIP well. During the freeze season, the modeled maximum difference of ice thickness using different snowfall scenarios ranged from 0.53&#8211;0.61 m. Snow cover delayed ice surface and ice bottom melting by 45 and 24 days, respectively. The modeled snow ice and superimposed ice accounted for 4&#8211;23% and 5&#8211;8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05&#8211;2 m, respectively.</p>

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Interannual variability in sea-ice thickness in the pack-ice zone off Lützow–Holm Bay, East Antarctica

Polar Science ◽

10.1016/j.polar.2015.10.003 ◽

2016 ◽

Vol 10 (1) ◽

pp. 43-51 ◽

Cited By ~ 4

Author(s):

Fuko Sugimoto ◽

Takeshi Tamura ◽

Haruhito Shimoda ◽

Shotaro Uto ◽

Daisuke Simizu ◽

...

Keyword(s):

Sea Ice ◽

Interannual Variability ◽

East Antarctica ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Pack Ice

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Comparing methods of measuring sea-ice density in the East Antarctic

Annals of Glaciology ◽

10.3189/2015aog69a814 ◽

2015 ◽

Vol 56 (69) ◽

pp. 77-82 ◽

Cited By ~ 9

Author(s):

Jennifer K. Hutchings ◽

Petra Heil ◽

Oliver Lecomte ◽

Roger Stevens ◽

Adam Steer ◽

...

Keyword(s):

Sea Ice ◽

Error Estimates ◽

East Antarctica ◽

Ice Thickness ◽

First Year ◽

Density Estimates ◽

Sea Ice Thickness ◽

The Mean ◽

Volume Method ◽

Mass Volume

AbstractRemotely sensed derivation of sea-ice thickness requires sea·ice density. Sea-ice density was estimated with three techniques during the second Sea Ice Physics and Ecosystem eXperimett (SIPEX-II, September-November 2012, East Antarctica). The sea ice was first-year highly deformed, mean thicknsss 1.2 m with layers, consistent with rafting, and 6-7/10 columnar ice and 3/10 granular ice. Ice density was found to be lower than values (900-920 kg m−3 used previously to derive ice thickness,, with columnar ice mean density of 870 kg m− 3. At two different ice stations the mean density of the ice was 800 kg m–3, the lower density reflecting a high percentage of porous granular ice at the second station. Error estimates for mass/volume and liquid/solid water methods are presented. With 0.1 m long, 0.1 m core samples, the error on individual density estimates is 28 kg m-3. Errors are larger for smaller machined blocks. Errors increase to 46 kg m-3 if the liquid/solid volume method is used. The mass/vouume method has a low bias due to brine drainage of at least 5%. Bulk densities estimated from ice and snow measurements along 100 m transects were high, and likely unrealistic as the assumption of isostatcc balance is not suitable over these length scales in deformed ice.

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Tenuous Correlation between Snow Depth or Sea Ice Thickness and C- or X-Band Backscattering in Nunavik Fjords of the Hudson Strait

Remote Sensing ◽

10.3390/rs13040768 ◽

2021 ◽

Vol 13 (4) ◽

pp. 768

Author(s):

Sophie Dufour-Beauséjour ◽

Monique Bernier ◽

Jérome Simon ◽

Saeid Homayouni ◽

Véronique Gilbert ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Linear Regression Analysis ◽

Ice Thickness ◽

First Year ◽

Sea Ice Thickness ◽

X Band ◽

Bayesian Linear Regression ◽

Linear Correlations ◽

Better Than

Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscattering in the C- and X-band. Snow depth and ice thickness were directly measured in three fjords of the Hudson Strait from 2015 to 2018 in April or May. Bayesian linear regression analysis was used to investigate their relationship with RADARSAT-2 (C-band) or TerraSAR-X (X-band). Polarimetric ratios and the Cloude–Pottier decomposition parameters were explored along with the HH, HV and VV bands. Linear correlations were generally no higher than 0.3 except for a special case in May 2018. The co-polarization ratio did not perform better than the backscattering coefficients.

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The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

10.5194/egusphere-egu21-12240 ◽

2021 ◽

Author(s):

Isolde Glissenaar ◽

Jack Landy ◽

Alek Petty ◽

Nathan Kurtz ◽

Julienne Stroeve

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Satellite Altimetry ◽

Region Of Interest ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Baffin Bay ◽

The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

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Interannual sea ice thickness variability in the Bay of Bothnia

The Cryosphere ◽

10.5194/tc-12-3459-2018 ◽

2018 ◽

Vol 12 (11) ◽

pp. 3459-3476 ◽

Cited By ~ 5

Author(s):

Iina Ronkainen ◽

Jonni Lehtiranta ◽

Mikko Lensu ◽

Eero Rinne ◽

Jari Haapala ◽

...

Keyword(s):

Sea Ice ◽

Interannual Variability ◽

Thickness Distribution ◽

Ice Thickness ◽

Data Sets ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Thickness Variability ◽

Fast Ice ◽

Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

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Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

10.5194/egusphere-egu21-13779 ◽

2021 ◽

Author(s):

Alek Petty ◽

Nicole Keeney ◽

Alex Cabaj ◽

Paul Kushner ◽

Nathan Kurtz ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Cloud ◽

Ice Drift ◽

Thickness Variability ◽

Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite&#8208; 2 (ICESat&#8208;2) mission was launched in September 2018 and is now providing routine, very high&#8208;resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA&#8217;s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

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A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice

The Cryosphere ◽

10.5194/tc-4-583-2010 ◽

2010 ◽

Vol 4 (4) ◽

pp. 583-592 ◽

Cited By ~ 59

Author(s):

L. Kaleschke ◽

N. Maaß ◽

C. Haas ◽

S. Hendricks ◽

G. Heygster ◽

...

Keyword(s):

Sea Ice ◽

Brightness Temperature ◽

Ice Thickness ◽

First Year ◽

Sea Ice Thickness ◽

Model Calculations ◽

Airborne Measurements ◽

Low Salinity ◽

Research Aircraft ◽

L Band

Abstract. In preparation for the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, we investigated the potential of L-band (1.4 GHz) radiometry to measure sea-ice thickness. Sea-ice brightness temperature was measured at 1.4 GHz and ice thickness was measured along nearly coincident flight tracks during the SMOS Sea-Ice campaign in the Bay of Bothnia in March 2007. A research aircraft was equipped with the L-band Radiometer EMIRAD and coordinated with helicopter based electromagnetic induction (EM) ice thickness measurements. We developed a three layer (ocean-ice-atmosphere) dielectric slab model for the calculation of ice thickness from brightness temperature. The dielectric properties depend on the relative brine volume which is a function of the bulk ice salinity and temperature. The model calculations suggest a thickness sensitivity of up to 1.5 m for low-salinity (multi-year or brackish) sea-ice. For Arctic first year ice the modelled thickness sensitivity is less than half a meter. It reduces to a few centimeters for temperatures approaching the melting point. The campaign was conducted under unfavorable melting conditions and the spatial overlap between the L-band and EM-measurements was relatively small. Despite these disadvantageous conditions we demonstrate the possibility to measure the sea-ice thickness with the certain limitation up to 1.5 m. The ice thickness derived from SMOS measurements would be complementary to ESA's CryoSat-2 mission in terms of the error characteristics and the spatiotemporal coverage. The relative error for the SMOS ice thickness retrieval is expected to be not less than about 20%.

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Freeboard height and snow depth observed by floating GPS on land-fast sea ice in Nella Fjord, Antarctica

Annals of Glaciology ◽

10.1017/aog.2020.41 ◽

2020 ◽

Vol 61 (82) ◽

pp. 227-239

Author(s):

Qingchuan Zhang ◽

Fei Li ◽

Jintao Lei ◽

Shengkai Zhang ◽

Zhuoming Ding ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Single Point ◽

Pressure Gauge ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Zhongshan Station ◽

Ocean Topography ◽

Bottom Pressure Gauge ◽

The Relationship

AbstractAlthough altimeters have been widely used to monitor the spatiotemporal variation of sea-ice thickness, they are unable to separate sea-ice freeboard from snow depth. We use a floating GPS deployed on sea ice to derive the freeboard and snow depth near China's Zhongshan Station. Our results show that the standalone floating GPS can monitor freeboard with a precision of 4.2 cm. If time-varying dynamic ocean topography provided by, for example, a bottom pressure gauge is available, then the precision of GPS-derived freeboard can improve to 1.3 cm. The daily snow depth inverted by GPS interferometric reflectometry captures three precipitation events during our experiment, showing that the floating GPS can monitor the variation in snow depth and observe the freeboard variation at the same time. By studying the relationship between freeboard, snow depth and sea-ice thickness, we find that sea-ice thickness will be greatly underestimated by the negative single-point freeboard under the assumption of hydrostatic equilibrium. As a supplement to existing technologies, the GPS-derived freeboard and snow depth can be used both to evaluate the altimeter observations directly and to improve our understanding of the real-time variation of freeboard and snow depth in the experimental area.

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Seasonal development of the sea-ice thickness distribution in East Antarctica: measurements from upward-looking sonar

Annals of Glaciology ◽

10.3189/172756401781818167 ◽

2001 ◽

Vol 33 ◽

pp. 177-180 ◽

Cited By ~ 14

Author(s):

A. P. Worby ◽

G. M. Bush ◽

I. Allison

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

East Antarctica ◽

Seasonal Development ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Seasonal Evolution ◽

Pack Ice ◽

The Mean ◽

Prototype Instrument

AbstractUpward-looking sonar (ULS) data are presented from a prototype instrument deployed at 63° 18’ S, 107°49’ E in 1994. These data show the seasonal evolution of the ice-draft distribution from May when predominantly thin ice is present, through October when substantially thicker ice has been formed by deformation. The mean ice draft reaches a maximum in August at 1.21 m, the same month in which ship-based observations from the same region show a peak in ice thickness. The observed distribution from ULS data is only for drafts > 0.3 m due to data losses caused by the low acoustic reflectivity of actively forming ice. The spring distributions show very little development of drafts > 3.0 m, and it is hypothesized that this is due to the cyclical nature of deformation in the East Antarctic pack-ice zone, and that periods of sustained pressure required to form very thick ice are uncommon in this region

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